{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eb6b1eef-4edd-48cd-87f0-d8860572477f",
   "metadata": {},
   "source": [
    "# Electric Vehicle Market Analysis: Growth Drivers & Strategic Opportunities (2010-2024)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b54b43-114b-4542-8106-b06dbdf5f2d2",
   "metadata": {},
   "source": [
    "## Introsuction\n",
    "### Dateset\n",
    "This dataset provides a snapshot of the electric vehicle (EV) market in countried across the world from 2010 to 2024. It includes historical data on EV sales, stock, and market share, categorized by vehicle mode (e.g., cars), powertrain type (BEV, PHEV), and metric (e.g., stock share, sales share). The data helps in understanding the initial adoption trends of electric mobility.\n",
    "\n",
    "## Executive Summary\n",
    "### Key Findings\n",
    "**Market Opportunity**: France emerges as the #1 strategic opportunity (310K vehicles, 68% CAGR) combining scale with sustainable growth, while explosive markets like UAE (228% CAGR) offer early-stage positioning opportunities.\\\n",
    "**Technology Intelligence**: Policy drives technology adoption more than consumer preference. Germany shows 17.7pp BEV share swings despite market growth, while USA maintains stable 75-80% BEV preference across market cycles.\\\n",
    "**Strategic Insights**: Market growth and BEV adoption are not correlated - markets can expand 260% while BEV share declines 9.5%, revealing policy timing as critical for market entry success.\n",
    "\n",
    "### Strategic Recommendations\n",
    "**Stable Growth Markets**: USA offers predictable BEV-focused strategy\\\n",
    "**High-Reward Opportunities**: Germany/France combine scale with growth but require policy-sensitive timing\\\n",
    "**Emerging Markets**: UAE/Romania provide 200%+ growth for early-stage positioning\\\n",
    "**Technology Strategy**: Maintain BEV/PHEV portfolio flexibility in policy-sensitive European markets\n",
    "\n",
    "### Business Impact\n",
    "This analysis provides data-driven guidance for investment timing, market entry strategy, and technology portfolio decisions across 52 global EV markets, quantifying both opportunity and policy risk.\n",
    "\n",
    "### Objective\n",
    "#### Analyze global electric vehicle sales patterns from 2010-2024 to identify:\n",
    "Key growth drivers and market dynamics\\\n",
    "Geographic adoption patterns and opportunities\\\n",
    "Strategic insights for stakeholders (investors, manufacturers, policymakers)\n",
    "\n",
    "### Key Questions\n",
    "What are the primary drivers of EV market growth?\\\n",
    "Which markets show highest growth potential?\\\n",
    "How has competitive landscape evolved?\\\n",
    "What strategic opportunities exist for different stakeholders?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4882645f-365b-4d7a-8ebf-afe2e83e4612",
   "metadata": {},
   "source": [
    "## 1. Dataset Overview\n",
    "### Import necessary libraries\n",
    "The dataset is imported and systematically explored and quality-checked to ensure that subsequent analyses (such as visualization and modeling) are built on a reliable data foundation. This enables further in-depth exploration of the development patterns and driving factors in the electric vehicle market through time series analysis, regional comparisons, clustering, or regression modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5a1d5f5-88a7-48ea-a222-798b4850b095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== DATASET OVERVIEW ===\n",
      "Shape: 3798 rows, 8 columns\n",
      "Memory usage: 1.28 MB\n",
      "\n",
      "=== COLUMN INFORMATION ===\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3798 entries, 0 to 3797\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   region      3798 non-null   object \n",
      " 1   category    3798 non-null   object \n",
      " 2   parameter   3798 non-null   object \n",
      " 3   mode        3798 non-null   object \n",
      " 4   powertrain  3798 non-null   object \n",
      " 5   year        3798 non-null   int64  \n",
      " 6   unit        3798 non-null   object \n",
      " 7   value       3798 non-null   float64\n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 237.5+ KB\n",
      "\n",
      "=== FIRST FEW ROWS ===\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>category</th>\n",
       "      <th>parameter</th>\n",
       "      <th>mode</th>\n",
       "      <th>powertrain</th>\n",
       "      <th>year</th>\n",
       "      <th>unit</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV sales</td>\n",
       "      <td>Cars</td>\n",
       "      <td>BEV</td>\n",
       "      <td>2011</td>\n",
       "      <td>Vehicles</td>\n",
       "      <td>49.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV stock share</td>\n",
       "      <td>Cars</td>\n",
       "      <td>EV</td>\n",
       "      <td>2011</td>\n",
       "      <td>percent</td>\n",
       "      <td>0.00039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV sales share</td>\n",
       "      <td>Cars</td>\n",
       "      <td>EV</td>\n",
       "      <td>2011</td>\n",
       "      <td>percent</td>\n",
       "      <td>0.00650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV stock</td>\n",
       "      <td>Cars</td>\n",
       "      <td>BEV</td>\n",
       "      <td>2011</td>\n",
       "      <td>Vehicles</td>\n",
       "      <td>49.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Historical</td>\n",
       "      <td>EV stock</td>\n",
       "      <td>Cars</td>\n",
       "      <td>BEV</td>\n",
       "      <td>2012</td>\n",
       "      <td>Vehicles</td>\n",
       "      <td>220.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      region    category       parameter  mode powertrain  year      unit  \\\n",
       "0  Australia  Historical        EV sales  Cars        BEV  2011  Vehicles   \n",
       "1  Australia  Historical  EV stock share  Cars         EV  2011   percent   \n",
       "2  Australia  Historical  EV sales share  Cars         EV  2011   percent   \n",
       "3  Australia  Historical        EV stock  Cars        BEV  2011  Vehicles   \n",
       "4  Australia  Historical        EV stock  Cars        BEV  2012  Vehicles   \n",
       "\n",
       "       value  \n",
       "0   49.00000  \n",
       "1    0.00039  \n",
       "2    0.00650  \n",
       "3   49.00000  \n",
       "4  220.00000  "
      ]
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== STATISTICAL SUMMARY ===\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3798.000000</td>\n",
       "      <td>3.798000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.369932</td>\n",
       "      <td>1.009542e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.809226</td>\n",
       "      <td>8.184402e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.500000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2.300000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1.900000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2021.000000</td>\n",
       "      <td>6.800000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2023.000000</td>\n",
       "      <td>2.800000e+07</td>\n",
       "    </tr>\n",
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       "              year         value\n",
       "count  3798.000000  3.798000e+03\n",
       "mean   2017.369932  1.009542e+05\n",
       "std       3.809226  8.184402e+05\n",
       "min    2010.000000  1.500000e-05\n",
       "25%    2014.000000  2.300000e+00\n",
       "50%    2018.000000  1.900000e+02\n",
       "75%    2021.000000  6.800000e+03\n",
       "max    2023.000000  2.800000e+07"
      ]
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     "output_type": "stream",
     "text": [
      "\n",
      "=== DATA QUALITY CHECK ===\n",
      "Missing values per column:\n",
      "region        0\n",
      "category      0\n",
      "parameter     0\n",
      "mode          0\n",
      "powertrain    0\n",
      "year          0\n",
      "unit          0\n",
      "value         0\n",
      "dtype: int64\n",
      "\n",
      "Duplicate rows: 0\n"
     ]
    }
   ],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Load dataset\n",
    "df = pd.read_csv('electric car sales (2010-2024).csv')\n",
    "\n",
    "print(\"=== DATASET OVERVIEW ===\")\n",
    "print(f\"Shape: {df.shape[0]} rows, {df.shape[1]} columns\")\n",
    "print(f\"Memory usage: {df.memory_usage(deep=True).sum() / 1024**2:.2f} MB\")\n",
    "\n",
    "print(\"\\n=== COLUMN INFORMATION ===\")\n",
    "df.info()\n",
    "\n",
    "print(\"\\n=== FIRST FEW ROWS ===\")\n",
    "display(df.head())\n",
    "\n",
    "print(\"\\n=== STATISTICAL SUMMARY ===\")\n",
    "display(df.describe())\n",
    "\n",
    "print(\"\\n=== DATA QUALITY CHECK ===\")\n",
    "print(\"Missing values per column:\")\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print(\"\\nDuplicate rows:\", df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54139207-fb05-4ee6-953b-e1a732d9f1cf",
   "metadata": {},
   "source": [
    "#### Conduct business context analysis and logical validation on the electric vehicle market dataset. \n",
    "By counting the distribution of categorical variables (such as the number of parameters, vehicle types, and powertrain types) and displaying sample data of key indicators, it helps to understand the data structure and business implications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a2a9318-8511-4f8b-b4e3-7b14056f69b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== COLUMN ANALYSIS ===\n",
      "\n",
      "region:\n",
      "  - Type: object\n",
      "  - Unique values: 52\n",
      "  - Missing: 0 (0.0%)\n",
      "  - Sample values: ['Australia', 'Austria', 'Belgium', 'Brazil', 'Bulgaria']\n",
      "\n",
      "category:\n",
      "  - Type: object\n",
      "  - Unique values: 1\n",
      "  - Missing: 0 (0.0%)\n",
      "  - Sample values: ['Historical']\n",
      "\n",
      "parameter:\n",
      "  - Type: object\n",
      "  - Unique values: 7\n",
      "  - Missing: 0 (0.0%)\n",
      "  - Sample values: ['EV sales', 'EV stock share', 'EV sales share', 'EV stock', 'Electricity demand']\n",
      "\n",
      "mode:\n",
      "  - Type: object\n",
      "  - Unique values: 1\n",
      "  - Missing: 0 (0.0%)\n",
      "  - Sample values: ['Cars']\n",
      "\n",
      "powertrain:\n",
      "  - Type: object\n",
      "  - Unique values: 4\n",
      "  - Missing: 0 (0.0%)\n",
      "  - Sample values: ['BEV', 'EV', 'PHEV', 'FCEV']\n",
      "\n",
      "year:\n",
      "  - Type: int64\n",
      "  - Unique values: 14\n",
      "  - Missing: 0 (0.0%)\n",
      "  - Range: 2010 to 2023\n",
      "\n",
      "unit:\n",
      "  - Type: object\n",
      "  - Unique values: 5\n",
      "  - Missing: 0 (0.0%)\n",
      "  - Sample values: ['Vehicles', 'percent', 'GWh', 'Milion barrels per day', 'Oil displacement, million lge']\n",
      "\n",
      "value:\n",
      "  - Type: float64\n",
      "  - Unique values: 814\n",
      "  - Missing: 0 (0.0%)\n",
      "  - Range: 1.4999999621e-05 to 28000000.0\n"
     ]
    }
   ],
   "source": [
    "print(\"=== COLUMN ANALYSIS ===\")\n",
    "for col in df.columns:\n",
    "    print(f\"\\n{col}:\")\n",
    "    print(f\"  - Type: {df[col].dtype}\")\n",
    "    print(f\"  - Unique values: {df[col].nunique()}\")\n",
    "    print(f\"  - Missing: {df[col].isnull().sum()} ({df[col].isnull().mean()*100:.1f}%)\")\n",
    "    \n",
    "    if df[col].dtype in ['object']:\n",
    "        print(f\"  - Sample values: {df[col].unique()[:5].tolist()}\")\n",
    "    elif df[col].dtype in ['int64', 'float64']:\n",
    "        print(f\"  - Range: {df[col].min()} to {df[col].max()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f26bb9-a171-4000-be27-b189ae8abd03",
   "metadata": {},
   "source": [
    "### In-depth Analysis of Business Dimensions\n",
    "Count and analyze key categorical variables to help understand the data coverage, including regional distribution, parameter types, vehicle modes, powertrain types, and time coverage. Then, sample and validate business logic check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "371bb52c-240f-49a0-ba26-3916ace48dab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== BUSINESS CONTEXT ANALYSIS ===\n",
      "Regions (52):\n",
      "region\n",
      "World                154\n",
      "Europe               154\n",
      "USA                  144\n",
      "China                132\n",
      "Rest of the world    124\n",
      "EU27                 112\n",
      "Germany              109\n",
      "Belgium              107\n",
      "France               105\n",
      "Japan                104\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Parameters - Key Metrics (7):\n",
      "parameter\n",
      "EV sales                         1342\n",
      "EV stock                         1145\n",
      "EV sales share                    603\n",
      "EV stock share                    465\n",
      "Oil displacement Mbd               84\n",
      "Oil displacement, million lge      84\n",
      "Electricity demand                 75\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Vehicle Modes (1):\n",
      "mode\n",
      "Cars    3798\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Powertrains (4):\n",
      "powertrain\n",
      "EV      1311\n",
      "BEV     1078\n",
      "PHEV     922\n",
      "FCEV     487\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Units - Measurement Types (5):\n",
      "unit\n",
      "Vehicles                         2487\n",
      "percent                          1068\n",
      "Milion barrels per day             84\n",
      "Oil displacement, million lge      84\n",
      "GWh                                75\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Year Coverage:\n",
      "year\n",
      "2010    135\n",
      "2011    182\n",
      "2012    203\n",
      "2013    223\n",
      "2014    238\n",
      "2015    287\n",
      "2016    289\n",
      "2017    294\n",
      "2018    301\n",
      "2019    319\n",
      "2020    321\n",
      "2021    338\n",
      "2022    336\n",
      "2023    332\n",
      "Name: count, dtype: int64\n",
      "\n",
      "=== BUSINESS LOGIC VALIDATION ===\n",
      "Sample combinations:\n",
      "\n",
      "EV sales:\n",
      "      region  year      unit  value\n",
      "0  Australia  2011  Vehicles   49.0\n",
      "6  Australia  2012  Vehicles   80.0\n",
      "\n",
      "EV stock share:\n",
      "      region  year     unit    value\n",
      "1  Australia  2011  percent  0.00039\n",
      "8  Australia  2012  percent  0.00240\n",
      "\n",
      "EV sales share:\n",
      "      region  year     unit   value\n",
      "2  Australia  2011  percent  0.0065\n",
      "7  Australia  2012  percent  0.0300\n"
     ]
    }
   ],
   "source": [
    "print(\"=== BUSINESS CONTEXT ANALYSIS ===\")\n",
    "\n",
    "print(f\"Regions ({df['region'].nunique()}):\")\n",
    "print(df['region'].value_counts().head(10))\n",
    "\n",
    "print(f\"\\nParameters - Key Metrics ({df['parameter'].nunique()}):\")\n",
    "print(df['parameter'].value_counts())\n",
    "\n",
    "print(f\"\\nVehicle Modes ({df['mode'].nunique()}):\")\n",
    "print(df['mode'].value_counts())\n",
    "\n",
    "print(f\"\\nPowertrains ({df['powertrain'].nunique()}):\")\n",
    "print(df['powertrain'].value_counts())\n",
    "\n",
    "print(f\"\\nUnits - Measurement Types ({df['unit'].nunique()}):\")\n",
    "print(df['unit'].value_counts())\n",
    "\n",
    "print(f\"\\nYear Coverage:\")\n",
    "print(df['year'].value_counts().sort_index())\n",
    "\n",
    "# Business logic check\n",
    "print(\"\\n=== BUSINESS LOGIC VALIDATION ===\")\n",
    "print(\"Sample combinations:\")\n",
    "for param in df['parameter'].unique()[:3]:\n",
    "    sample = df[df['parameter'] == param].head(2)\n",
    "    print(f\"\\n{param}:\")\n",
    "    print(sample[['region', 'year', 'unit', 'value']].to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51da91b-c8d3-4230-be4f-b5067dfbd1c6",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e035499-8d45-4a7e-9814-b3b2575a54f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "region_sales_data = df.groupby(['region', 'year'])['value'].sum().reset_index()\n",
    "region_sales_data.rename(columns={'value': 'total_sales'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6c076fd-abc1-4916-af8b-3450134510f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "region_sales_data['cumulative_sales'] = region_year_sales.groupby('region')['total_sales'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e772873b-be1f-4058-b0d0-4a71c0cfe4df",
   "metadata": {},
   "outputs": [],
   "source": [
    "region_sales_data['yoy_growth'] = region_year_sales.groupby('region')['total_sales'].pct_change() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5594cff2-10d1-4c09-9771-e412240a84d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "region_sales_data['yoy_growth'] = region_year_sales.groupby('region')['total_sales'].pct_change() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "42920a3e-8f31-443c-8029-2c8f99272e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>cumulative_sales</th>\n",
       "      <th>yoy_growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2011</td>\n",
       "      <td>98.00689</td>\n",
       "      <td>98.00689</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>550.03240</td>\n",
       "      <td>648.03929</td>\n",
       "      <td>461.218094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2013</td>\n",
       "      <td>880.03860</td>\n",
       "      <td>1528.07789</td>\n",
       "      <td>59.997593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2014</td>\n",
       "      <td>3200.17400</td>\n",
       "      <td>4728.25189</td>\n",
       "      <td>263.640186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2015</td>\n",
       "      <td>5360.22700</td>\n",
       "      <td>10088.47889</td>\n",
       "      <td>67.497986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2016</td>\n",
       "      <td>6370.18700</td>\n",
       "      <td>16458.66589</td>\n",
       "      <td>18.841739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2017</td>\n",
       "      <td>9600.31200</td>\n",
       "      <td>26058.97789</td>\n",
       "      <td>50.706910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2018</td>\n",
       "      <td>14500.49700</td>\n",
       "      <td>40559.47489</td>\n",
       "      <td>51.041935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2019</td>\n",
       "      <td>29801.34000</td>\n",
       "      <td>70360.81489</td>\n",
       "      <td>105.519438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020</td>\n",
       "      <td>33901.28000</td>\n",
       "      <td>104262.09489</td>\n",
       "      <td>13.757569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      region  year  total_sales  cumulative_sales  yoy_growth\n",
       "0  Australia  2011     98.00689          98.00689         NaN\n",
       "1  Australia  2012    550.03240         648.03929  461.218094\n",
       "2  Australia  2013    880.03860        1528.07789   59.997593\n",
       "3  Australia  2014   3200.17400        4728.25189  263.640186\n",
       "4  Australia  2015   5360.22700       10088.47889   67.497986\n",
       "5  Australia  2016   6370.18700       16458.66589   18.841739\n",
       "6  Australia  2017   9600.31200       26058.97789   50.706910\n",
       "7  Australia  2018  14500.49700       40559.47489   51.041935\n",
       "8  Australia  2019  29801.34000       70360.81489  105.519438\n",
       "9  Australia  2020  33901.28000      104262.09489   13.757569"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_sales_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45bf280c-1335-4c21-836a-8ef87315f1b0",
   "metadata": {},
   "source": [
    "## 2. EV market analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "56c625a1-1e9b-44f7-8a57-52768a35120d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== TOP EV MARKETS ANALYSIS ===\n",
      "Top 10 EV Markets in 2023:\n",
      "region\n",
      "World             13808900.0\n",
      "China              8100520.0\n",
      "Europe             3300820.0\n",
      "EU27               2450770.0\n",
      "USA                1393000.0\n",
      "Germany             700260.0\n",
      "France              470310.0\n",
      "United Kingdom      450025.0\n",
      "Belgium             193009.0\n",
      "Canada              171013.0\n",
      "Name: value, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Start with this high-impact analysis\n",
    "print(\"=== TOP EV MARKETS ANALYSIS ===\")\n",
    "\n",
    "# Focus on EV sales for market sizing\n",
    "sales_data = df[df['parameter'] == 'EV sales'].copy()\n",
    "\n",
    "# Latest year market sizes\n",
    "latest_year = sales_data['year'].max()\n",
    "market_leaders = sales_data[sales_data['year'] == latest_year].groupby('region')['value'].sum().sort_values(ascending=False)\n",
    "\n",
    "print(f\"Top 10 EV Markets in {latest_year}:\")\n",
    "print(market_leaders.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d45f21c-f2e6-48fc-9e64-a97c79cdd5d3",
   "metadata": {},
   "source": [
    "Growth rate analysis\n",
    "#### The CAGR analysis quantifies the growth trends and potential of electric vehicle markets in various countries, providing data-driven support for business decisions:\n",
    "**Strategic Level**: Identify high-potential markets to optimize global resource allocation.\\\n",
    "**Operational Level**: Compare the growth efficiency of different markets to adjust product strategies (e.g., prioritize BEV or PHEV).\\\n",
    "**Risk Management Level**: Monitor policy changes in high-CAGR markets (e.g., subsidy reduction may lead to slowdowns)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9f423d22-e076-443a-9777-4eef51f121af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== GROWTH RATE ANALYSIS ===\n",
      "\n",
      "Top 15 Fastest Growing Markets (CAGR %):\n",
      "                  region   cagr  latest_sales\n",
      "45  United Arab Emirates  228.6        5900.0\n",
      "9             Costa Rica  143.2        4900.0\n",
      "11                Cyprus  142.1         510.0\n",
      "36               Romania  123.6       15000.0\n",
      "18                Greece  117.2       10000.0\n",
      "19               Hungary  109.2        5800.0\n",
      "2                Belgium  105.5      100000.0\n",
      "23                Israel  104.4       49000.0\n",
      "7                  China   99.5         520.0\n",
      "10               Croatia   96.1        1600.0\n",
      "17               Germany   88.2      180000.0\n",
      "44               Turkiye   81.8        2700.0\n",
      "35              Portugal   80.0           2.0\n",
      "26                 Korea   79.2       12000.0\n",
      "3                 Brazil   78.7       33000.0\n",
      "\n",
      "=== COUNTRY-LEVEL GROWTH RATES ===\n",
      "\n",
      "Largest Markets with Growth Rates:\n",
      "            region  latest_sales   cagr\n",
      "16          France      310000.0   67.9\n",
      "46  United Kingdom      310000.0    1.4\n",
      "47             USA      290000.0    7.3\n",
      "17         Germany      180000.0   88.2\n",
      "42          Sweden      110000.0   -5.2\n",
      "2          Belgium      100000.0  105.5\n",
      "33          Norway      100000.0  -27.0\n",
      "25           Japan       88000.0   26.7\n",
      "0        Australia       87000.0   57.0\n",
      "24           Italy       70000.0   76.8\n"
     ]
    }
   ],
   "source": [
    "# Growth rate analysis\n",
    "\n",
    "# Get clean regional data (avoid double-counting)\n",
    "sales_data = df[df['parameter'] == 'EV sales'].copy()\n",
    "\n",
    "# Exclude aggregated regions for country-level analysis\n",
    "country_level = sales_data[~sales_data['region'].isin(['World', 'Europe', 'EU27', 'Rest of the world'])]\n",
    "\n",
    "# Calculate CAGR for major markets\n",
    "def calculate_cagr(region_data):\n",
    "    if len(region_data) < 2:\n",
    "        return None\n",
    "    first_year = region_data['year'].min()\n",
    "    last_year = region_data['year'].max()\n",
    "    first_value = region_data[region_data['year'] == first_year]['value'].iloc[0]\n",
    "    last_value = region_data[region_data['year'] == last_year]['value'].iloc[0]\n",
    "    \n",
    "    if first_value <= 0:\n",
    "        return None\n",
    "    \n",
    "    years = last_year - first_year\n",
    "    cagr = (last_value / first_value) ** (1/years) - 1\n",
    "    return cagr * 100\n",
    "\n",
    "\n",
    "growth_rates = []\n",
    "\n",
    "for region in country_level['region'].unique():\n",
    "    region_data = country_level[country_level['region'] == region].sort_values('year')\n",
    "    if len(region_data) >= 3:  # Need multiple years for meaningful CAGR\n",
    "        cagr = calculate_cagr(region_data)\n",
    "        if cagr is not None:\n",
    "            latest_sales = region_data['value'].iloc[-1]\n",
    "            growth_rates.append({\n",
    "                'region': region,\n",
    "                'cagr': cagr,\n",
    "                'latest_sales': latest_sales,\n",
    "                'years_data': len(region_data)\n",
    "            })\n",
    "\n",
    "print(f\"\\n=== GROWTH RATE ANALYSIS ===\")\n",
    "growth_df = pd.DataFrame(growth_rates).sort_values('cagr', ascending=False)\n",
    "print(\"\\nTop 15 Fastest Growing Markets (CAGR %):\")\n",
    "print(growth_df.head(15)[['region', 'cagr', 'latest_sales']].round(1))\n",
    "\n",
    "print(f\"\\n=== COUNTRY-LEVEL GROWTH RATES ===\")\n",
    "print(\"\\nLargest Markets with Growth Rates:\")\n",
    "large_markets = growth_df[growth_df['latest_sales'] > 50000].sort_values('latest_sales', ascending=False)\n",
    "print(large_markets[['region', 'latest_sales', 'cagr']].head(10).round(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e3dd49e4-dc06-4593-a53b-682ad997c0e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== STRATEGIC MARKET MATRIX ===\n",
      "Strategic Market Categories:\n",
      "\n",
      "Rising Stars (High Growth + Emerging Scale):\n",
      "  United Arab Emirates: 5,900 vehicles, 228.6% CAGR\n",
      "  Costa Rica: 4,900 vehicles, 143.2% CAGR\n",
      "  Cyprus: 510 vehicles, 142.1% CAGR\n",
      "  Romania: 15,000 vehicles, 123.6% CAGR\n",
      "  Greece: 10,000 vehicles, 117.2% CAGR\n",
      "\n",
      "Stars (High Growth + High Scale):\n",
      "  Belgium: 100,000 vehicles, 105.5% CAGR\n",
      "  Germany: 180,000 vehicles, 88.2% CAGR\n",
      "  France: 310,000 vehicles, 67.9% CAGR\n",
      "\n",
      "Question Marks (Moderate Growth + Small Scale):\n",
      "  Portugal: 2 vehicles, 80.0% CAGR\n",
      "  Korea: 12,000 vehicles, 79.2% CAGR\n",
      "  Brazil: 33,000 vehicles, 78.7% CAGR\n",
      "  Latvia: 360 vehicles, 77.8% CAGR\n",
      "  Italy: 70,000 vehicles, 76.8% CAGR\n",
      "\n",
      "Cash Cows (High Scale + Moderate Growth):\n",
      "  USA: 290,000 vehicles, 7.3% CAGR\n",
      "  United Kingdom: 310,000 vehicles, 1.4% CAGR\n",
      "  Sweden: 110,000 vehicles, -5.2% CAGR\n",
      "  Norway: 100,000 vehicles, -27.0% CAGR\n",
      "\n",
      "=== TOP INVESTMENT OPPORTUNITIES (Opportunity Score) ===\n",
      "(Score = Market Size × Growth Rate)\n",
      "         region  latest_sales   cagr  opportunity_score\n",
      "16       France      310000.0   67.9              210.5\n",
      "17      Germany      180000.0   88.2              158.7\n",
      "2       Belgium      100000.0  105.5              105.5\n",
      "24        Italy       70000.0   76.8               53.8\n",
      "23       Israel       49000.0  104.4               51.2\n",
      "0     Australia       87000.0   57.0               49.6\n",
      "41        Spain       65000.0   66.4               43.2\n",
      "31  Netherlands       46000.0   69.0               31.7\n",
      "3        Brazil       33000.0   78.7               26.0\n",
      "25        Japan       88000.0   26.7               23.5\n"
     ]
    }
   ],
   "source": [
    "print(\"=== STRATEGIC MARKET MATRIX ===\")\n",
    "\n",
    "# Create strategic categories\n",
    "def categorize_market(row):\n",
    "    sales = row['latest_sales']\n",
    "    cagr = row['cagr']\n",
    "    \n",
    "    if sales >= 100000 and cagr >= 50:\n",
    "        return \"Stars (High Growth + High Scale)\"\n",
    "    elif sales >= 100000 and cagr < 50:\n",
    "        return \"Cash Cows (High Scale + Moderate Growth)\"\n",
    "    elif sales < 100000 and cagr >= 80:\n",
    "        return \"Rising Stars (High Growth + Emerging Scale)\"\n",
    "    elif sales < 100000 and cagr < 80:\n",
    "        return \"Question Marks (Moderate Growth + Small Scale)\"\n",
    "    else:\n",
    "        return \"Other\"\n",
    "\n",
    "growth_df['strategic_category'] = growth_df.apply(categorize_market, axis=1)\n",
    "\n",
    "print(\"Strategic Market Categories:\")\n",
    "for category in growth_df['strategic_category'].unique():\n",
    "    category_markets = growth_df[growth_df['strategic_category'] == category]\n",
    "    print(f\"\\n{category}:\")\n",
    "    for _, market in category_markets.head(5).iterrows():\n",
    "        print(f\"  {market['region']}: {market['latest_sales']:,.0f} vehicles, {market['cagr']:.1f}% CAGR\")\n",
    "\n",
    "# Calculate market opportunity score\n",
    "growth_df['opportunity_score'] = (growth_df['latest_sales'] / 1000) * (growth_df['cagr'] / 100)\n",
    "growth_df = growth_df.sort_values('opportunity_score', ascending=False)\n",
    "\n",
    "print(\"\\n=== TOP INVESTMENT OPPORTUNITIES (Opportunity Score) ===\")\n",
    "print(\"(Score = Market Size × Growth Rate)\")\n",
    "print(growth_df[['region', 'latest_sales', 'cagr', 'opportunity_score']].head(10).round(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef06273f-44f7-46c0-8ca7-b84d570c48d8",
   "metadata": {},
   "source": [
    "## 3. Visualization focus on EV market"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4abce25-bb9c-4cb5-9384-7ba15984cca6",
   "metadata": {},
   "source": [
    "### Bar Plot: Year and Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e887bae5-0afd-4074-8b4b-ac97da6c613f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Year and Value Bar Plot')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create visualization\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(x='year',y='value', data=df)\n",
    "plt.title(\"Year and Value Bar Plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a734e5a4-3c18-4ada-9364-6726854f2a05",
   "metadata": {},
   "source": [
    "### Pie Chat: Parameter types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7491436c-35dd-4fa7-bad5-8c13b2c32ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'parameter Pie Chat')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "colors = ['#FF9999', '#66B3FF', '#99FF99', '#FFCC99', '#C2C2F0', '#FFB6C1', '#D3D3D3']\n",
    "df['parameter'].value_counts().plot(kind='pie', autopct='1%.2f%%', colors=colors,startangle=140)\n",
    "plt.title(\"parameter Pie Chat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c747b1e5-0a12-4999-9964-2dd83b78aca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hist Plot')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TDwUEBJhvv12YAADA1cnrgehihmHI7XYrOjpaTqdTubm55rKqqipt2bJF/fv3lyTFxcXJz8/Po6aoqEj79+83axISEuRyubRr1y6zZufOnXK5XGYNAACwNq++ZTZr1iwlJycrMjJSZ86cUVZWljZv3qycnBzZbDalp6drzpw56tKli7p06aI5c+bo2muv1ejRoyVJdrtd48aN0/Tp09WxY0eFhIRoxowZ6tGjh/nWWbdu3TRs2DCNHz9eixcvliRNmDBBKSkpvGEGAAAkeTkQnThxQmlpaSoqKpLdblfPnj2Vk5OjIUOGSJKeeOIJVVZWauLEiSotLVW/fv30/vvvKygoyNzGvHnz5Ovrq1GjRqmyslKJiYlavny5fHx8zJpVq1Zp6tSp5ttoqampWrhwYcseLAAAaLVshmEY3m6iLSgvL5fdbpfL5WrS54k++ugjxcXF6bnszYq+9bZ6rXv0QIGeHjFA+fn5uv3225usJwAArhZX+vu71T1DBAAA0NIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPK8GogyMzPVt29fBQUFKSwsTPfdd58OHz7sUTN27FjZbDaPKT4+3qPG7XZrypQpCg0NVfv27ZWamqrjx4971JSWliotLU12u112u11paWkqKytr7kMEAABtgFcD0ZYtWzRp0iTt2LFDubm5+u6775SUlKSzZ8961A0bNkxFRUXmtG7dOo/l6enpys7OVlZWlrZt26aKigqlpKSourrarBk9erQKCgqUk5OjnJwcFRQUKC0trUWOEwAAtG6+3tx5Tk6Ox/yyZcsUFham/Px83XXXXeZ4QECAnE5nrdtwuVxaunSpVqxYocGDB0uSVq5cqcjISG3YsEFDhw7VoUOHlJOTox07dqhfv36SpCVLlighIUGHDx9WTExMMx0hAABoC1rVM0Qul0uSFBIS4jG+efNmhYWFqWvXrho/frxKSkrMZfn5+Tp37pySkpLMsYiICMXGxiovL0+StH37dtntdjMMSVJ8fLzsdrtZczG3263y8nKPCQAAXJ1aTSAyDEPTpk3THXfcodjYWHM8OTlZq1at0saNG/XSSy9p9+7dGjRokNxutySpuLhY/v7+6tChg8f2HA6HiouLzZqwsLAa+wwLCzNrLpaZmWk+b2S32xUZGdlUhwoAAFoZr35k9kOTJ0/WJ598om3btnmMP/jgg+bPsbGx6tOnj6KiorR27VqNHDmyzu0ZhiGbzWbO//Dnump+aObMmZo2bZo5X15eTigCAOAq1SruEE2ZMkXvvfeeNm3apE6dOl2yNjw8XFFRUTpy5Igkyel0qqqqSqWlpR51JSUlcjgcZs2JEydqbOvkyZNmzcUCAgIUHBzsMQEAgKuTVwORYRiaPHmy1qxZo40bNyo6Ovqy65w+fVqFhYUKDw+XJMXFxcnPz0+5ublmTVFRkfbv36/+/ftLkhISEuRyubRr1y6zZufOnXK5XGYNAACwLq9+ZDZp0iStXr1af//73xUUFGQ+z2O329WuXTtVVFQoIyND999/v8LDw3Xs2DHNmjVLoaGhGjFihFk7btw4TZ8+XR07dlRISIhmzJihHj16mG+ddevWTcOGDdP48eO1ePFiSdKECROUkpLCG2YAAMC7gWjRokWSpAEDBniML1u2TGPHjpWPj4/27dunt956S2VlZQoPD9fAgQP19ttvKygoyKyfN2+efH19NWrUKFVWVioxMVHLly+Xj4+PWbNq1SpNnTrVfBstNTVVCxcubP6DBAAArZ5XA5FhGJdc3q5dO61fv/6y2wkMDNSCBQu0YMGCOmtCQkK0cuXKevcIAACufq3ioWoAAABvIhABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLa1Aguummm3T69Oka42VlZbrpppsa3RQAAEBLalAgOnbsmKqrq2uMu91u/ec//2l0UwAAAC3Jtz7F7733nvnz+vXrZbfbzfnq6mp98MEH6ty5c5M1BwAA0BLqFYjuu+8+SZLNZtOYMWM8lvn5+alz58566aWXmqw5AACAllCvQHT+/HlJUnR0tHbv3q3Q0NBmaQoAAKAl1SsQXXD06NGm7gMAAMBrGhSIJOmDDz7QBx98oJKSEvPO0QVvvPFGoxsDAABoKQ0KRLNnz9Yf/vAH9enTR+Hh4bLZbE3dFwAAQItpUCB67bXXtHz5cqWlpTV1PwAAAC2uQd9DVFVVpf79+zd655mZmerbt6+CgoIUFham++67T4cPH/aoMQxDGRkZioiIULt27TRgwAAdOHDAo8btdmvKlCkKDQ1V+/btlZqaquPHj3vUlJaWKi0tTXa7XXa7XWlpaSorK2v0MQAAgLavQYHo0Ucf1erVqxu98y1btmjSpEnasWOHcnNz9d133ykpKUlnz541a1544QXNnTtXCxcu1O7du+V0OjVkyBCdOXPGrElPT1d2draysrK0bds2VVRUKCUlxePLI0ePHq2CggLl5OQoJydHBQUF3OECAACSGviR2bfffqvXX39dGzZsUM+ePeXn5+exfO7cuVe0nZycHI/5ZcuWKSwsTPn5+brrrrtkGIbmz5+vp556SiNHjpQkvfnmm3I4HFq9erUee+wxuVwuLV26VCtWrNDgwYMlSStXrlRkZKQ2bNigoUOH6tChQ8rJydGOHTvUr18/SdKSJUuUkJCgw4cPKyYmpiGnAQAAXCUadIfok08+0W233aZrrrlG+/fv1969e82poKCgwc24XC5JUkhIiKTvX+8vLi5WUlKSWRMQEKC7775beXl5kqT8/HydO3fOoyYiIkKxsbFmzfbt22W3280wJEnx8fGy2+1mDQAAsK4G3SHatGlTU/chwzA0bdo03XHHHYqNjZUkFRcXS5IcDodHrcPh0BdffGHW+Pv7q0OHDjVqLqxfXFyssLCwGvsMCwszay7mdrvldrvN+fLy8gYeGQAAaO0adIeoOUyePFmffPKJ/vKXv9RYdvFr/YZhXPZV/4traqu/1HYyMzPNB7DtdrsiIyOv5DAAAEAb1KA7RAMHDrxkINm4cWO9tjdlyhS999572rp1qzp16mSOO51OSd/f4QkPDzfHS0pKzLtGTqdTVVVVKi0t9bhLVFJSYr4J53Q6deLEiRr7PXnyZI27TxfMnDlT06ZNM+fLy8sJRQAAXKUadIfotttuU69evcype/fuqqqq0kcffaQePXpc8XYMw9DkyZO1Zs0abdy4UdHR0R7Lo6Oj5XQ6lZuba45VVVVpy5YtZtiJi4uTn5+fR01RUZH2799v1iQkJMjlcmnXrl1mzc6dO+Vyuer8+oCAgAAFBwd7TAAA4OrUoDtE8+bNq3U8IyNDFRUVV7ydSZMmafXq1fr73/+uoKAg83keu92udu3ayWazKT09XXPmzFGXLl3UpUsXzZkzR9dee61Gjx5t1o4bN07Tp09Xx44dFRISohkzZqhHjx7mW2fdunXTsGHDNH78eC1evFiSNGHCBKWkpPCGGQAAaPjfMqvNL37xC/3kJz/Riy++eEX1ixYtkiQNGDDAY3zZsmUaO3asJOmJJ55QZWWlJk6cqNLSUvXr10/vv/++goKCzPp58+bJ19dXo0aNUmVlpRITE7V8+XL5+PiYNatWrdLUqVPNt9FSU1O1cOHCRhwtAAC4WjRpINq+fbsCAwOvuN4wjMvW2Gw2ZWRkKCMjo86awMBALViwQAsWLKizJiQkRCtXrrzi3gAAgHU0KBBd+JLECwzDUFFRkfbs2aNnnnmmSRoDAABoKQ0KRHa73WP+mmuuUUxMjP7whz94fEEiAABAW9CgQLRs2bKm7gMAAMBrGvUMUX5+vg4dOiSbzabu3burd+/eTdUXAABAi2lQICopKdHPf/5zbd68Wddff70Mw5DL5dLAgQOVlZWlG264oan7BAAAaDYN+mLGKVOmqLy8XAcOHNDXX3+t0tJS7d+/X+Xl5Zo6dWpT9wgAANCsGnSHKCcnRxs2bFC3bt3Mse7du+uVV17hoWoAANDmNOgO0fnz5+Xn51dj3M/PT+fPn290UwAAAC2pQYFo0KBB+vWvf62vvvrKHPvPf/6j3/zmN0pMTGyy5gAAAFpCgwLRwoULdebMGXXu3Fk333yzbrnlFkVHR+vMmTOX/LZoAACA1qhBzxBFRkbqo48+Um5urj799FMZhqHu3bubf0wVAACgLanXHaKNGzeqe/fuKi8vlyQNGTJEU6ZM0dSpU9W3b1/deuut+vDDD5ulUQAAgOZSr0A0f/58jR8/XsHBwTWW2e12PfbYY5o7d26TNQcAANAS6hWIPv74Yw0bNqzO5UlJScrPz290UwAAAC2pXoHoxIkTtb5uf4Gvr69OnjzZ6KYAAABaUr0C0Y9+9CPt27evzuWffPKJwsPDG90UAABAS6pXILrnnnv0+9//Xt9++22NZZWVlXr22WeVkpLSZM0BAAC0hHq9dv/0009rzZo16tq1qyZPnqyYmBjZbDYdOnRIr7zyiqqrq/XUU081V68AAADNol6ByOFwKC8vT7/61a80c+ZMGYYhSbLZbBo6dKheffVVORyOZmkUAACgudT7ixmjoqK0bt06lZaW6rPPPpNhGOrSpYs6dOjQHP0BAAA0uwZ9U7UkdejQQX379m3KXgAAALyiQX/LDAAA4GpCIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJbn1UC0detWDR8+XBEREbLZbHr33Xc9lo8dO1Y2m81jio+P96hxu92aMmWKQkND1b59e6Wmpur48eMeNaWlpUpLS5PdbpfdbldaWprKysqa+egAAEBb4dVAdPbsWfXq1UsLFy6ss2bYsGEqKioyp3Xr1nksT09PV3Z2trKysrRt2zZVVFQoJSVF1dXVZs3o0aNVUFCgnJwc5eTkqKCgQGlpac12XAAAoG3x9ebOk5OTlZycfMmagIAAOZ3OWpe5XC4tXbpUK1as0ODBgyVJK1euVGRkpDZs2KChQ4fq0KFDysnJ0Y4dO9SvXz9J0pIlS5SQkKDDhw8rJiamaQ8KAAC0Oa3+GaLNmzcrLCxMXbt21fjx41VSUmIuy8/P17lz55SUlGSORUREKDY2Vnl5eZKk7du3y263m2FIkuLj42W3282a2rjdbpWXl3tMAADg6tSqA1FycrJWrVqljRs36qWXXtLu3bs1aNAgud1uSVJxcbH8/f3VoUMHj/UcDoeKi4vNmrCwsBrbDgsLM2tqk5mZaT5zZLfbFRkZ2YRHBgAAWhOvfmR2OQ8++KD5c2xsrPr06aOoqCitXbtWI0eOrHM9wzBks9nM+R/+XFfNxWbOnKlp06aZ8+Xl5YQiAACuUq36DtHFwsPDFRUVpSNHjkiSnE6nqqqqVFpa6lFXUlIih8Nh1pw4caLGtk6ePGnW1CYgIEDBwcEeEwAAuDq1qUB0+vRpFRYWKjw8XJIUFxcnPz8/5ebmmjVFRUXav3+/+vfvL0lKSEiQy+XSrl27zJqdO3fK5XKZNQAAwNq8+pFZRUWFPvvsM3P+6NGjKigoUEhIiEJCQpSRkaH7779f4eHhOnbsmGbNmqXQ0FCNGDFCkmS32zVu3DhNnz5dHTt2VEhIiGbMmKEePXqYb51169ZNw4YN0/jx47V48WJJ0oQJE5SSksIbZgAAQJKXA9GePXs0cOBAc/7CMztjxozRokWLtG/fPr311lsqKytTeHi4Bg4cqLfffltBQUHmOvPmzZOvr69GjRqlyspKJSYmavny5fLx8TFrVq1apalTp5pvo6Wmpl7yu48AAIC1eDUQDRgwQIZh1Ll8/fr1l91GYGCgFixYoAULFtRZExISopUrVzaoRwAAcPVrU88QAQAANAcCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDyvBqKtW7dq+PDhioiIkM1m07vvvuux3DAMZWRkKCIiQu3atdOAAQN04MABjxq3260pU6YoNDRU7du3V2pqqo4fP+5RU1paqrS0NNntdtntdqWlpamsrKyZjw4AALQVXg1EZ8+eVa9evbRw4cJal7/wwguaO3euFi5cqN27d8vpdGrIkCE6c+aMWZOenq7s7GxlZWVp27ZtqqioUEpKiqqrq82a0aNHq6CgQDk5OcrJyVFBQYHS0tKa/fgAAEDb4OvNnScnJys5ObnWZYZhaP78+Xrqqac0cuRISdKbb74ph8Oh1atX67HHHpPL5dLSpUu1YsUKDR48WJK0cuVKRUZGasOGDRo6dKgOHTqknJwc7dixQ/369ZMkLVmyRAkJCTp8+LBiYmJa5mABAECr1WqfITp69KiKi4uVlJRkjgUEBOjuu+9WXl6eJCk/P1/nzp3zqImIiFBsbKxZs337dtntdjMMSVJ8fLzsdrtZUxu3263y8nKPCQAAXJ1abSAqLi6WJDkcDo9xh8NhLisuLpa/v786dOhwyZqwsLAa2w8LCzNrapOZmWk+c2S32xUZGdmo4wEAAK1Xqw1EF9hsNo95wzBqjF3s4pra6i+3nZkzZ8rlcplTYWFhPTsHAABtRasNRE6nU5Jq3MUpKSkx7xo5nU5VVVWptLT0kjUnTpyosf2TJ0/WuPv0QwEBAQoODvaYAADA1anVBqLo6Gg5nU7l5uaaY1VVVdqyZYv69+8vSYqLi5Ofn59HTVFRkfbv32/WJCQkyOVyadeuXWbNzp075XK5zBoAAGBtXn3LrKKiQp999pk5f/ToURUUFCgkJEQ33nij0tPTNWfOHHXp0kVdunTRnDlzdO2112r06NGSJLvdrnHjxmn69Onq2LGjQkJCNGPGDPXo0cN866xbt24aNmyYxo8fr8WLF0uSJkyYoJSUFN4wAwAAkrwciPbs2aOBAwea89OmTZMkjRkzRsuXL9cTTzyhyspKTZw4UaWlperXr5/ef/99BQUFmevMmzdPvr6+GjVqlCorK5WYmKjly5fLx8fHrFm1apWmTp1qvo2Wmppa53cfAQAA67EZhmF4u4m2oLy8XHa7XS6Xq0mfJ/roo48UFxen57I3K/rW2+q17tEDBXp6xADl5+fr9ttvb7KeAAC4Wlzp7+9W+wwRAABASyEQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy2vVgSgjI0M2m81jcjqd5nLDMJSRkaGIiAi1a9dOAwYM0IEDBzy24Xa7NWXKFIWGhqp9+/ZKTU3V8ePHW/pQAABAK9aqA5Ek3XrrrSoqKjKnffv2mcteeOEFzZ07VwsXLtTu3bvldDo1ZMgQnTlzxqxJT09Xdna2srKytG3bNlVUVCglJUXV1dXeOBwAANAK+Xq7gcvx9fX1uCt0gWEYmj9/vp566imNHDlSkvTmm2/K4XBo9erVeuyxx+RyubR06VKtWLFCgwcPliStXLlSkZGR2rBhg4YOHdqixwIAAFqnVn+H6MiRI4qIiFB0dLR+/vOf69///rck6ejRoyouLlZSUpJZGxAQoLvvvlt5eXmSpPz8fJ07d86jJiIiQrGxsWZNXdxut8rLyz0mAABwdWrVgahfv3566623tH79ei1ZskTFxcXq37+/Tp8+reLiYkmSw+HwWMfhcJjLiouL5e/vrw4dOtRZU5fMzEzZ7XZzioyMbMIjAwAArUmrDkTJycm6//771aNHDw0ePFhr166V9P1HYxfYbDaPdQzDqDF2sSupmTlzplwulzkVFhY28CgAAEBr16oD0cXat2+vHj166MiRI+ZzRRff6SkpKTHvGjmdTlVVVam0tLTOmroEBAQoODjYYwIAAFenNhWI3G63Dh06pPDwcEVHR8vpdCo3N9dcXlVVpS1btqh///6SpLi4OPn5+XnUFBUVaf/+/WYNAABAq37LbMaMGRo+fLhuvPFGlZSU6LnnnlN5ebnGjBkjm82m9PR0zZkzR126dFGXLl00Z84cXXvttRo9erQkyW63a9y4cZo+fbo6duyokJAQzZgxw/wIDgAAQGrlgej48eN66KGHdOrUKd1www2Kj4/Xjh07FBUVJUl64oknVFlZqYkTJ6q0tFT9+vXT+++/r6CgIHMb8+bNk6+vr0aNGqXKykolJiZq+fLl8vHx8dZhAQCAVqZVB6KsrKxLLrfZbMrIyFBGRkadNYGBgVqwYIEWLFjQxN0BAICrRZt6hggAAKA5EIgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlWSoQvfrqq4qOjlZgYKDi4uL04YcferslAADQCvh6u4GW8vbbbys9PV2vvvqqfvrTn2rx4sVKTk7WwYMHdeONN3q7vUY5dOhQg9YLDQ1t88cOAEBTsEwgmjt3rsaNG6dHH31UkjR//nytX79eixYtUmZmppe7a5iykydks9n0i1/8okHrBwQG6p2//U3h4eH1XpcwBQC4mlgiEFVVVSk/P19PPvmkx3hSUpLy8vK81FXjfVPukmEY+q///j+6ObZnvdY9vGe7Vs6ZpZSUlAbtuzFhyu12KyAgoEH7bcy6UuOC3JdffqlTp061+H4BAM3PEoHo1KlTqq6ulsPh8Bh3OBwqLi6udR232y23223Ou1wuSVJ5eXmT9lZRUSFJOnbgY337zdl6rfvV5/+SJFV9W1nvdc98fVqGYWhI2mMKj7qpXuseP3JIG99e3uAwJdkkGV5Y9/sgt+Ktt2pcC5dz4sQJPfLII/r2229bdL+SdM011+j8+fMN2i/rsi7rsm5bWdfpdMrpdDZo3Uu58HvbMC7zu8OwgP/85z+GJCMvL89j/LnnnjNiYmJqXefZZ5819P1vXiYmJiYmJqY2PhUWFl4yK1jiDlFoaKh8fHxq3A0qKSmp81/sM2fO1LRp08z58+fP6+uvv1bHjh1ls9marLfy8nJFRkaqsLBQwcHBTbZdK+EcNg7nr/E4h43D+Ws8zmHdDMPQmTNnFBERcck6SwQif39/xcXFKTc3VyNGjDDHc3Nzde+999a6TkBAQI1nVa6//vpm6zE4OJiLuJE4h43D+Ws8zmHjcP4aj3NYO7vdftkaSwQiSZo2bZrS0tLUp08fJSQk6PXXX9eXX36pxx9/3NutAQAAL7NMIHrwwQd1+vRp/eEPf1BRUZFiY2O1bt06RUVFebs1AADgZZYJRJI0ceJETZw40dtteAgICNCzzz7bqFfJrY5z2Dicv8bjHDYO56/xOIeNZzOMy72HBgAAcHWz1N8yAwAAqA2BCAAAWB6BCAAAWB6BCAAAWB6BqAW8+uqrio6OVmBgoOLi4vThhx9esn7Lli2Ki4tTYGCgbrrpJr322mst1GnrVZ9zuHnzZtlsthrTp59+2oIdtx5bt27V8OHDFRERIZvNpnffffey63AN/n/1PX9cf54yMzPVt29fBQUFKSwsTPfdd58OHz582fW4Bv+/hpxDrsP6IxA1s7ffflvp6el66qmntHfvXt15551KTk7Wl19+WWv90aNHdc899+jOO+/U3r17NWvWLE2dOlXvvPNOC3feetT3HF5w+PBhFRUVmVOXLl1aqOPW5ezZs+rVq5cWLlx4RfVcg57qe/4u4Pr73pYtWzRp0iTt2LFDubm5+u6775SUlKSzZ+v+g9Rcg54acg4v4Dqsh6b586moy09+8hPj8ccf9xj78Y9/bDz55JO11j/xxBPGj3/8Y4+xxx57zIiPj2+2Hlu7+p7DTZs2GZKM0tLSFuiubZFkZGdnX7KGa7BuV3L+uP4uraSkxJBkbNmypc4arsFLu5JzyHVYf9whakZVVVXKz89XUlKSx3hSUpLy8vJqXWf79u016ocOHao9e/bo3LlzzdZra9WQc3hB7969FR4ersTERG3atKk527yqcA02Da6/2rlcLklSSEhInTVcg5d2JefwAq7DK0cgakanTp1SdXW1HA6Hx7jD4VBxcXGt6xQXF9da/9133+nUqVPN1mtr1ZBzGB4ertdff13vvPOO1qxZo5iYGCUmJmrr1q0t0XKbxzXYOFx/dTMMQ9OmTdMdd9yh2NjYOuu4But2peeQ67D+LPWnO7zFZrN5zBuGUWPscvW1jVtJfc5hTEyMYmJizPmEhAQVFhbqxRdf1F133dWsfV4tuAYbjuuvbpMnT9Ynn3yibdu2XbaWa7B2V3oOuQ7rjztEzSg0NFQ+Pj417mSUlJTU+NfPBU6ns9Z6X19fdezYsdl6ba0acg5rEx8fryNHjjR1e1clrsGmx/UnTZkyRe+99542bdqkTp06XbKWa7B29TmHteE6vDQCUTPy9/dXXFyccnNzPcZzc3PVv3//WtdJSEioUf/++++rT58+8vPza7ZeW6uGnMPa7N27V+Hh4U3d3lWJa7DpWfn6MwxDkydP1po1a7Rx40ZFR0dfdh2uQU8NOYe1sfJ1eEW89ji3RWRlZRl+fn7G0qVLjYMHDxrp6elG+/btjWPHjhmGYRhPPvmkkZaWZtb/+9//Nq699lrjN7/5jXHw4EFj6dKlhp+fn/G3v/3NW4fgdfU9h/PmzTOys7ONf/3rX8b+/fuNJ5980pBkvPPOO946BK86c+aMsXfvXmPv3r2GJGPu3LnG3r17jS+++MIwDK7By6nv+eP68/SrX/3KsNvtxubNm42ioiJz+uabb8warsFLa8g55DqsPwJRC3jllVeMqKgow9/f37j99ts9XpUcM2aMcffdd3vUb9682ejdu7fh7+9vdO7c2Vi0aFELd9z61OccPv/888bNN99sBAYGGh06dDDuuOMOY+3atV7ounW48PrtxdOYMWMMw+AavJz6nj+uP0+1nTtJxrJly8warsFLa8g55DqsP5th/L8n1QAAACyKZ4gAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAWFrnzp01f/58b7cBWNbWrVs1fPhwRUREyGaz6d13363X+hkZGbLZbDWm9u3b12s7BCIAAOA1Z8+eVa9evbRw4cIGrT9jxgwVFRV5TN27d9cDDzxQr+0QiAAAgNckJyfrueee08iRI2tdXlVVpSeeeEI/+tGP1L59e/Xr10+bN282l1933XVyOp3mdOLECR08eFDjxo2rVx8EIgBt1uLFi/WjH/1I58+f9xhPTU3VmDFj9Pnnn+vee++Vw+HQddddp759+2rDhg11bu/YsWOy2WwqKCgwx8rKymSz2Tz+B3zw4EHdc889uu666+RwOJSWlqZTp0419eEBkPRf//Vf+uc//6msrCx98skneuCBBzRs2DAdOXKk1vo///nP6tq1q+6888567YdABKDNeuCBB3Tq1Clt2rTJHCstLdX69ev18MMPq6KiQvfcc482bNigvXv3aujQoRo+fLi+/PLLBu+zqKhId999t2677Tbt2bNHOTk5OnHihEaNGtUUhwTgBz7//HP95S9/0V//+lfdeeeduvnmmzVjxgzdcccdWrZsWY16t9utVatW1fvukCT5NkXDAOANISEhGjZsmFavXq3ExERJ0l//+leFhIQoMTFRPj4+6tWrl1n/3HPPKTs7W++9954mT57coH0uWrRIt99+u+bMmWOOvfHGG4qMjNS//vUvde3atXEHBcD00UcfyTCMGv9dud1udezYsUb9mjVrdObMGT3yyCP13heBCECb9vDDD2vChAl69dVXFRAQoFWrVunnP/+5fHx8dPbsWc2ePVv/+7//q6+++krfffedKisrG3WHKD8/X5s2bdJ1111XY9nnn39OIAKa0Pnz5+Xj46P8/Hz5+Ph4LKvtv8E///nPSklJkdPprPe+CEQA2rThw4fr/PnzWrt2rfr27asPP/xQc+fOlST99re/1fr16/Xiiy/qlltuUbt27fSzn/1MVVVVtW7rmmu+f4rAMAxz7Ny5cx4158+f1/Dhw/X888/XWD88PLypDguApN69e6u6ulolJSWXfSbo6NGj2rRpk957770G7YtABKBNa9eunUaOHKlVq1bps88+U9euXRUXFydJ+vDDDzV27FiNGDFCklRRUaFjx47Vua0bbrhB0vfPCfXu3VuSPB6wlqTbb79d77zzjjp37ixfX/4XCjRWRUWFPvvsM3P+6NGjKigoUEhIiLp27aqHH35YjzzyiF566SX17t1bp06d0saNG9WjRw/dc8895npvvPGGwsPDlZyc3KA+eKgaQJv38MMPa+3atXrjjTf0i1/8why/5ZZbtGbNGhUUFOjjjz/W6NGja7yR9kPt2rVTfHy8/vSnP+ngwYPaunWrnn76aY+aSZMm6euvv9ZDDz2kXbt26d///rfef/99/fKXv1R1dXWzHSNwtdqzZ4969+5t/iNk2rRp6t27t37/+99LkpYtW6ZHHnlE06dPV0xMjFJTU7Vz505FRkaa2zh//ryWL1+usWPH1vho7UrxzxsAbd6gQYMUEhKiw4cPa/To0eb4vHnz9Mtf/lL9+/dXaGiofve736m8vPyS23rjjTf0y1/+Un369FFMTIxeeOEFJSUlmcsjIiL0z3/+U7/73e80dOhQud1uRUVFadiwYeZHbgCu3IABAzw+pr6Yn5+fZs+erdmzZ9dZc80116iwsLBRfdiMS3UBAABgAfxzBgAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWN7/BcN+eUpwmiy2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='value',data=df, bins=30, kde=False, color='skyblue')\n",
    "plt.title(\"Hist Plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c285c67-e118-44ad-a83f-74567974e799",
   "metadata": {},
   "source": [
    "### Bar Chart: Top Countries by Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca1567fa-e46e-4a08-a217-08e033b28148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the most recent year\n",
    "latest_year = df['year'].max()\n",
    "top_countries = df[df['year'] == latest_year].groupby('region')['value'].sum().reset_index()\n",
    "top_countries = top_countries.sort_values(by='value', ascending=False).head(15)\n",
    "\n",
    "plt.figure(figsize=(14, 6))\n",
    "sns.barplot(data=top_countries, x='value', y='region', palette='magma')\n",
    "plt.title(f'Top 15 Countries by EV Sales in {latest_year}')\n",
    "plt.xlabel('EV Sales')\n",
    "plt.ylabel('Country')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00884dd4-e72b-4261-ac48-74b246ec41e8",
   "metadata": {},
   "source": [
    "### Line Plot: Powertrain Sales Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4bf23f62-9bda-4d1a-8322-bee8fccadb4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total sales by year and powertrain\n",
    "powertrain_trend = df.groupby(['year', 'powertrain'])['value'].sum().reset_index()\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(data=powertrain_trend, x='year', y='value', hue='powertrain', marker='o')\n",
    "plt.title('EV Sales by Powertrain Type Over Time')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sales')\n",
    "plt.legend(title='Powertrain')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ab613e2-62e9-466a-9840-a6a1e2cb595a",
   "metadata": {},
   "source": [
    "### Scatter: EV Mareket Strategic Positioning: Growth vs Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "464f270d-56d3-4af7-90a9-9378ee7f790f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 10))\n",
    "scatter = plt.scatter(growth_df['latest_sales'], growth_df['cagr'], \n",
    "                     s=growth_df['opportunity_score']*5, alpha=0.7)\n",
    "\n",
    "# Add strategic quadrant lines\n",
    "plt.axhline(y=50, color='red', linestyle='--', alpha=0.5)\n",
    "plt.axvline(x=100000, color='red', linestyle='--', alpha=0.5)\n",
    "\n",
    "# Annotate key markets\n",
    "for idx, row in growth_df.head(15).iterrows():\n",
    "    plt.annotate(row['region'], (row['latest_sales'], row['cagr']), \n",
    "                xytext=(5, 5), textcoords='offset points', fontsize=8)\n",
    "\n",
    "plt.xlabel('Market Size (2023 EV Sales)')\n",
    "plt.ylabel('Growth Rate (CAGR %)')\n",
    "plt.title('EV Market Strategic Positioning: Growth vs Scale')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5232ba2-8b76-4d7a-b67a-3982d27de9e5",
   "metadata": {},
   "source": [
    "## 4. Strategic Insights\n",
    "### Key Business Intelligence Revealed\n",
    "1. The \"Golden Triangle\" (Upper Right Quadrant):\n",
    "- Germany: 180K vehicles, 88% CAGR - European powerhouse\n",
    "- Belgium: 100K vehicles, 105% CAGR - Compact market efficiency\n",
    "- France: 310K vehicles, 68% CAGR - Optimal scale + momentum\n",
    "2. The \"Explosive Growth Frontier\" (Upper Left):\n",
    "- UAE: 5.9K vehicles, 228% CAGR - Gulf region transformation\n",
    "- Romania: 15K vehicles, 123% CAGR - Eastern European emergence\n",
    "- Costa Rica: 4.9K vehicles, 143% CAGR - Central America pioneer\n",
    "3. The \"Mature Market Reality\" (Lower Right):\n",
    "- USA: 290K vehicles, 7% CAGR - Large but slowing\n",
    "- UK: 310K vehicles, 1% CAGR - Market saturation signals\n",
    "- Norway: 100K vehicles, -27% CAGR - Post-incentive decline\n",
    "\n",
    "## For Different Stakeholder Groups\n",
    "### Investment Strategy Matrix:\n",
    "- Venture Capital/Early Stage:\n",
    "- Target: UAE, Costa Rica, Romania (Rising Stars)\n",
    "- Rationale: 100%+ growth rates, early market positioning\n",
    "### Risk: Small scale, regulatory dependency\n",
    "- Growth Equity/Expansion:\n",
    "- Target: Germany, Belgium, France (Stars)\n",
    "- Rationale: Proven scale + sustained growth\n",
    "### Risk: Increased competition, policy changes\n",
    "- Strategic Corporate Investment:\n",
    "- Target: USA, UK (Cash Cows)\n",
    "- Rationale: Market access, stable operations\n",
    "### Risk: Limited growth upside\n",
    "- Market Entry Timing Strategy\n",
    "- Immediate Entry Markets:\n",
    "- France (210.5 opportunity score) - Best balance of scale + growth\n",
    "- Germany (158.7 score) - European hub with strong momentum\n",
    "- Belgium (105.5 score) - High efficiency, strategic location\n",
    "### Watch & Wait Markets:\n",
    "- UAE - Monitor growth sustainability\n",
    "- USA - Wait for policy catalyst or technology breakthrough\n",
    "- Norway - Study maturity transition patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2df0da7e-29c6-4599-a169-de5f881ee66d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== TECHNOLOGY ADOPTION PATTERNS ===\n",
      "Technology Mix by Major Markets (2023):\n",
      "\n",
      "Germany (Total: 700,260):\n",
      "  FCEV: 260 (0.0%)\n",
      "  BEV: 520,000 (74.3%)\n",
      "  PHEV: 180,000 (25.7%)\n",
      "\n",
      "France (Total: 470,310):\n",
      "  FCEV: 310 (0.1%)\n",
      "  PHEV: 160,000 (34.0%)\n",
      "  BEV: 310,000 (65.9%)\n",
      "\n",
      "USA (Total: 1,393,000):\n",
      "  BEV: 1,100,000 (79.0%)\n",
      "  FCEV: 3,000 (0.2%)\n",
      "  PHEV: 290,000 (20.8%)\n",
      "\n",
      "United Kingdom (Total: 450,025):\n",
      "  PHEV: 140,000 (31.1%)\n",
      "  FCEV: 25 (0.0%)\n",
      "  BEV: 310,000 (68.9%)\n",
      "\n",
      "Belgium (Total: 193,009):\n",
      "  FCEV: 9 (0.0%)\n",
      "  BEV: 93,000 (48.2%)\n",
      "  PHEV: 100,000 (51.8%)\n"
     ]
    }
   ],
   "source": [
    "print(\"=== TECHNOLOGY ADOPTION PATTERNS ===\")\n",
    "\n",
    "# Analyze powertrain preferences by region\n",
    "tech_data = df[df['parameter'] == 'EV sales'].copy()\n",
    "tech_latest = tech_data[tech_data['year'] == tech_data['year'].max()]\n",
    "\n",
    "# Regional technology preferences\n",
    "print(\"Technology Mix by Major Markets (2023):\")\n",
    "major_markets = ['Germany', 'France', 'USA', 'United Kingdom', 'Belgium']\n",
    "\n",
    "for market in major_markets:\n",
    "    market_tech = tech_latest[tech_latest['region'] == market]\n",
    "    total_sales = market_tech['value'].sum()\n",
    "    print(f\"\\n{market} (Total: {total_sales:,.0f}):\")\n",
    "    \n",
    "    for powertrain in market_tech['powertrain'].unique():\n",
    "        powertrain_sales = market_tech[market_tech['powertrain'] == powertrain]['value'].sum()\n",
    "        share = (powertrain_sales / total_sales) * 100 if total_sales > 0 else 0\n",
    "        print(f\"  {powertrain}: {powertrain_sales:,.0f} ({share:.1f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca46f7e7-fd9a-4f68-9334-f1cacf6304fc",
   "metadata": {},
   "source": [
    "## Key Technology Insights Revealed\n",
    "### BEV Dominance Patterns:\n",
    "- USA: 79% BEV (1.1M vehicles) - Tesla effect + infrastructure scale\n",
    "- Germany: 74% BEV (520K vehicles) - Strong domestic manufacturing\n",
    "- France: 66% BEV (310K vehicles) - Policy incentives working\n",
    "- UK: 69% BEV (310K vehicles) - Island nation charging advantage\n",
    "### PHEV Strategic Markets:\n",
    "- Belgium: 52% PHEV (100K vehicles) - Unique market preference\n",
    "- France: 34% PHEV (160K vehicles) - Balanced technology approach\n",
    "- UK: 31% PHEV (140K vehicles) - Range anxiety hedge\n",
    "- Germany: 26% PHEV (180K vehicles) - Premium segment strength\n",
    "### FCEV Reality Check:\n",
    "- Negligible everywhere: <0.2% market share across all major markets\n",
    "- Infrastructure barrier: Hydrogen station deployment lag\n",
    "- Cost disadvantage: Battery technology advancement outpacing fuel cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f3fc3625-72b1-4c85-8acc-03b114a39c1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== TECHNOLOGY EVOLUTION TIMELINE ===\n",
      "\n",
      "Germany Technology Evolution:\n",
      "  BEV Share by Year:\n",
      "    2019: 58.2% (108,200 total vehicles)\n",
      "    2020: 48.7% (390,200 total vehicles)\n",
      "    2021: 52.1% (690,460 total vehicles)\n",
      "    2022: 56.6% (830,840 total vehicles)\n",
      "    2023: 74.3% (700,260 total vehicles)\n",
      "\n",
      "France Technology Evolution:\n",
      "  BEV Share by Year:\n",
      "    2019: 70.2% (64,060 total vehicles)\n",
      "    2020: 59.4% (185,210 total vehicles)\n",
      "    2021: 54.8% (310,014 total vehicles)\n",
      "    2022: 61.7% (340,200 total vehicles)\n",
      "    2023: 65.9% (470,310 total vehicles)\n",
      "\n",
      "USA Technology Evolution:\n",
      "  BEV Share by Year:\n",
      "    2019: 73.4% (327,000 total vehicles)\n",
      "    2020: 77.9% (295,200 total vehicles)\n",
      "    2021: 74.2% (633,100 total vehicles)\n",
      "    2022: 80.6% (992,700 total vehicles)\n",
      "    2023: 79.0% (1,393,000 total vehicles)\n",
      "\n",
      "United Kingdom Technology Evolution:\n",
      "  BEV Share by Year:\n",
      "    2019: 50.6% (75,068 total vehicles)\n",
      "    2020: 61.8% (178,057 total vehicles)\n",
      "    2021: 61.3% (310,012 total vehicles)\n",
      "    2022: 73.0% (370,007 total vehicles)\n",
      "    2023: 68.9% (450,025 total vehicles)\n",
      "\n",
      "=== REGIONAL TECHNOLOGY STRATEGY CLASSIFICATION ===\n",
      "\n",
      "BEV Maximalists (>75% BEV):\n",
      "  USA: 79.0%\n",
      "\n",
      "BEV Leaders (65-75% BEV):\n",
      "  Germany: 74.3%\n",
      "  France: 65.9%\n",
      "  United Kingdom: 68.9%\n"
     ]
    }
   ],
   "source": [
    "print(\"=== TECHNOLOGY EVOLUTION TIMELINE ===\")\n",
    "\n",
    "# Analyze BEV vs PHEV adoption over time\n",
    "tech_evolution = df[df['parameter'] == 'EV sales'].copy()\n",
    "\n",
    "# Focus on major markets for trend analysis\n",
    "major_markets = ['Germany', 'France', 'USA', 'United Kingdom']\n",
    "\n",
    "for market in major_markets:\n",
    "    print(f\"\\n{market} Technology Evolution:\")\n",
    "    market_data = tech_evolution[tech_evolution['region'] == market]\n",
    "    \n",
    "    # Calculate BEV share over time\n",
    "    yearly_totals = market_data.groupby('year')['value'].sum()\n",
    "    yearly_bev = market_data[market_data['powertrain'] == 'BEV'].groupby('year')['value'].sum()\n",
    "    \n",
    "    bev_share_timeline = (yearly_bev / yearly_totals * 100).round(1)\n",
    "    \n",
    "    print(\"  BEV Share by Year:\")\n",
    "    for year, share in bev_share_timeline.tail(5).items():\n",
    "        total = yearly_totals[year]\n",
    "        print(f\"    {year}: {share}% ({total:,.0f} total vehicles)\")\n",
    "\n",
    "print(\"\\n=== REGIONAL TECHNOLOGY STRATEGY CLASSIFICATION ===\")\n",
    "\n",
    "# Classify markets by technology preference\n",
    "tech_strategies = {\n",
    "    'BEV Maximalists (>75% BEV)': [],\n",
    "    'BEV Leaders (65-75% BEV)': [],\n",
    "    'Balanced Portfolio (40-65% BEV)': [],\n",
    "    'PHEV Preference (<40% BEV)': []\n",
    "}\n",
    "\n",
    "latest_tech = tech_evolution[tech_evolution['year'] == tech_evolution['year'].max()]\n",
    "\n",
    "for market in major_markets:\n",
    "    market_tech = latest_tech[latest_tech['region'] == market]\n",
    "    if not market_tech.empty:\n",
    "        total_sales = market_tech['value'].sum()\n",
    "        bev_sales = market_tech[market_tech['powertrain'] == 'BEV']['value'].sum()\n",
    "        bev_share = (bev_sales / total_sales) * 100 if total_sales > 0 else 0\n",
    "        \n",
    "        if bev_share >= 75:\n",
    "            tech_strategies['BEV Maximalists (>75% BEV)'].append(f\"{market}: {bev_share:.1f}%\")\n",
    "        elif bev_share >= 65:\n",
    "            tech_strategies['BEV Leaders (65-75% BEV)'].append(f\"{market}: {bev_share:.1f}%\")\n",
    "        elif bev_share >= 40:\n",
    "            tech_strategies['Balanced Portfolio (40-65% BEV)'].append(f\"{market}: {bev_share:.1f}%\")\n",
    "        else:\n",
    "            tech_strategies['PHEV Preference (<40% BEV)'].append(f\"{market}: {bev_share:.1f}%\")\n",
    "\n",
    "for strategy, markets in tech_strategies.items():\n",
    "    if markets:\n",
    "        print(f\"\\n{strategy}:\")\n",
    "        for market in markets:\n",
    "            print(f\"  {market}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ffb999cf-f0d5-4c6f-9a3b-ef11f590c5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization #2 - heat map\n",
    "# Technology preference visualization\n",
    "import seaborn as sns\n",
    "\n",
    "# Create technology mix matrix for visualization\n",
    "tech_matrix = latest_tech.pivot_table(\n",
    "    index='region', \n",
    "    columns='powertrain', \n",
    "    values='value', \n",
    "    fill_value=0\n",
    ")\n",
    "\n",
    "# Calculate percentages\n",
    "tech_matrix_pct = tech_matrix.div(tech_matrix.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Focus on major markets\n",
    "major_markets_matrix = tech_matrix_pct.loc[major_markets]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(major_markets_matrix, annot=True, fmt='.1f', cmap='RdYlBu_r')\n",
    "plt.title('Technology Adoption Patterns by Market (% Share)')\n",
    "plt.ylabel('Market')\n",
    "plt.xlabel('Powertrain Technology')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5dc84a4-3eb0-475b-a332-5ccb3fff69b7",
   "metadata": {},
   "source": [
    "## Strategic Technology Evolution Insights\n",
    "### Key Pattern: Policy-Driven Volatility, Not Linear Adoption\n",
    "- Germany's \"Policy Roller Coaster\":\n",
    "- 2019-2020: BEV share drops from 58% → 49% (PHEV incentives?)\n",
    "- 2020-2023: BEV recovery 49% → 74% (policy adjustment)\n",
    "- 2023 anomaly: Total market contraction despite BEV dominance\n",
    "### USA's \"Plateau Pattern\":\n",
    "- Consistent BEV leadership: 73-81% range over 5 years\n",
    "- Market expansion: 327K → 1.39M vehicles (326% growth)\n",
    "- Technology stability: Less policy volatility = more consistent adoption\n",
    "### UK's \"Peak & Retreat\":\n",
    "- 2022 peak: 73% BEV share (policy incentive peak?)\n",
    "- 2023 pullback: 69% BEV (incentive reduction/Brexit effects?)\n",
    "- Market growth: Continued expansion despite share volatility\n",
    "### France's \"Balanced Strategy\":\n",
    "- Most stable BEV share: 55-70% range\n",
    "- Consistent growth: 64K → 470K vehicles (634% expansion)\n",
    "- Strategic balance: Maintains PHEV option (33% share)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b88efae8-f7a7-4f98-8def-01a3e1cef429",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== MARKET DYNAMICS ANALYSIS ===\n",
      "\n",
      "Germany Market Dynamics:\n",
      "  Year | Total Sales | YoY Growth | BEV Share | BEV Share Change\n",
      "  -----|-------------|------------|-----------|------------------\n",
      "  2019 |     108,200 |      0.0% |    58.2% |     +0.0pp\n",
      "  2020 |     390,200 |    260.6% |    48.7% |     -9.5pp\n",
      "  2021 |     690,460 |     77.0% |    52.1% |     +3.4pp\n",
      "  2022 |     830,840 |     20.3% |    56.6% |     +4.4pp\n",
      "  2023 |     700,260 |    -15.7% |    74.3% |    +17.7pp\n",
      "\n",
      "France Market Dynamics:\n",
      "  Year | Total Sales | YoY Growth | BEV Share | BEV Share Change\n",
      "  -----|-------------|------------|-----------|------------------\n",
      "  2019 |      64,060 |      0.0% |    70.2% |     +0.0pp\n",
      "  2020 |     185,210 |    189.1% |    59.4% |    -10.9pp\n",
      "  2021 |     310,014 |     67.4% |    54.8% |     -4.6pp\n",
      "  2022 |     340,200 |      9.7% |    61.7% |     +6.9pp\n",
      "  2023 |     470,310 |     38.2% |    65.9% |     +4.2pp\n",
      "\n",
      "USA Market Dynamics:\n",
      "  Year | Total Sales | YoY Growth | BEV Share | BEV Share Change\n",
      "  -----|-------------|------------|-----------|------------------\n",
      "  2019 |     327,000 |      0.0% |    73.4% |     +0.0pp\n",
      "  2020 |     295,200 |     -9.7% |    77.9% |     +4.5pp\n",
      "  2021 |     633,100 |    114.5% |    74.2% |     -3.7pp\n",
      "  2022 |     992,700 |     56.8% |    80.6% |     +6.4pp\n",
      "  2023 |   1,393,000 |     40.3% |    79.0% |     -1.6pp\n",
      "\n",
      "United Kingdom Market Dynamics:\n",
      "  Year | Total Sales | YoY Growth | BEV Share | BEV Share Change\n",
      "  -----|-------------|------------|-----------|------------------\n",
      "  2019 |      75,068 |      0.0% |    50.6% |     +0.0pp\n",
      "  2020 |     178,057 |    137.2% |    61.8% |    +11.2pp\n",
      "  2021 |     310,012 |     74.1% |    61.3% |     -0.5pp\n",
      "  2022 |     370,007 |     19.4% |    73.0% |    +11.7pp\n",
      "  2023 |     450,025 |     21.6% |    68.9% |     -4.1pp\n",
      "\n",
      "=== STRATEGIC INSIGHTS ===\n",
      "Key observations:\n",
      "1. Market growth and BEV adoption are NOT perfectly correlated\n",
      "2. Policy changes create technology mix volatility\n",
      "3. USA shows most consistent BEV preference\n",
      "4. European markets more susceptible to policy-driven swings\n"
     ]
    }
   ],
   "source": [
    "print(\"=== MARKET DYNAMICS ANALYSIS ===\")\n",
    "\n",
    "# Analyze market growth vs technology evolution\n",
    "tech_evolution = df[df['parameter'] == 'EV sales'].copy()\n",
    "major_markets = ['Germany', 'France', 'USA', 'United Kingdom']\n",
    "\n",
    "for market in major_markets:\n",
    "    print(f\"\\n{market} Market Dynamics:\")\n",
    "    market_data = tech_evolution[tech_evolution['region'] == market]\n",
    "    \n",
    "    # Year-over-year analysis\n",
    "    yearly_totals = market_data.groupby('year')['value'].sum().sort_index()\n",
    "    yearly_bev = market_data[market_data['powertrain'] == 'BEV'].groupby('year')['value'].sum()\n",
    "    \n",
    "    print(\"  Year | Total Sales | YoY Growth | BEV Share | BEV Share Change\")\n",
    "    print(\"  -----|-------------|------------|-----------|------------------\")\n",
    "    \n",
    "    prev_total = None\n",
    "    prev_bev_share = None\n",
    "    \n",
    "    for year in yearly_totals.index[-5:]:  # Last 5 years\n",
    "        total = yearly_totals[year]\n",
    "        bev_sales = yearly_bev.get(year, 0)\n",
    "        bev_share = (bev_sales / total * 100) if total > 0 else 0\n",
    "        \n",
    "        # Calculate growth rates\n",
    "        yoy_growth = ((total / prev_total - 1) * 100) if prev_total else 0\n",
    "        bev_share_change = bev_share - prev_bev_share if prev_bev_share else 0\n",
    "        \n",
    "        print(f\"  {year} | {total:>11,.0f} | {yoy_growth:>8.1f}% | {bev_share:>7.1f}% | {bev_share_change:>+8.1f}pp\")\n",
    "        \n",
    "        prev_total = total\n",
    "        prev_bev_share = bev_share\n",
    "\n",
    "print(\"\\n=== STRATEGIC INSIGHTS ===\")\n",
    "print(\"Key observations:\")\n",
    "print(\"1. Market growth and BEV adoption are NOT perfectly correlated\")\n",
    "print(\"2. Policy changes create technology mix volatility\")\n",
    "print(\"3. USA shows most consistent BEV preference\")\n",
    "print(\"4. European markets more susceptible to policy-driven swings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7140bc99-1180-4c67-9a5f-6e83a6c8cdd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== FINAL STRATEGIC MARKET CLASSIFICATION ===\n",
      "Market Risk Assessment (Policy Sensitivity):\n",
      "============================================================\n",
      "Market          | BEV Volatility | Growth Volatility | Risk Level\n",
      "                | (Max pp change)| (Max YoY %)      |\n",
      "------------------------------------------------------------\n",
      "Germany         |          17.7 |            260.6 | High\n",
      "France          |          10.9 |            189.1 | Medium\n",
      "USA             |           6.4 |            114.5 | Low\n",
      "United Kingdom  |          11.7 |            137.2 | Medium-High\n",
      "\n",
      "=== INVESTMENT STRATEGY MATRIX ===\n",
      "\n",
      "🎯 STABLE GROWTH TARGETS (Low Policy Risk):\n",
      "   • USA: Consistent BEV preference + predictable growth\n",
      "   • Investment: BEV infrastructure, charging networks\n",
      "\n",
      "📈 HIGH-REWARD/HIGH-RISK (Policy-Sensitive Growth):\n",
      "   • Germany: Massive volatility = opportunity + risk\n",
      "   • Investment: Flexible tech portfolio, policy hedging\n",
      "\n",
      "⚖️ BALANCED PORTFOLIO MARKETS (Moderate Risk):\n",
      "   • France: Steady growth, manageable volatility\n",
      "   • UK: Strong growth but Brexit/policy uncertainty\n",
      "\n",
      "=== MARKET ENTRY STRATEGIC RECOMMENDATIONS ===\n",
      "\n",
      "Technology Strategy:\n",
      "   • Portfolio approach: BEV primary + PHEV backup in policy-sensitive markets\n",
      "   • USA: Can focus on BEV-only strategy due to policy stability\n",
      "   • Europe: Maintain technology flexibility due to policy volatility\n",
      "\n",
      "Market Timing:\n",
      "   • Germany: Enter during policy-favorable periods, expect volatility\n",
      "   • France: Steady entry strategy, consistent growth trajectory\n",
      "   • USA: Immediate market entry viable, stable policy environment\n",
      "   • UK: Monitor post-Brexit policy stabilization\n",
      "\n",
      "Risk Management:\n",
      "   • Diversify across markets with different policy cycles\n",
      "   • Monitor policy indicators: incentive levels, taxation changes\n",
      "   • Build scenario planning for policy-driven market shifts\n"
     ]
    }
   ],
   "source": [
    "print(\"=== FINAL STRATEGIC MARKET CLASSIFICATION ===\")\n",
    "\n",
    "# Calculate volatility metrics\n",
    "markets_analysis = {\n",
    "    'Germany': {'bev_volatility': 17.7, 'growth_volatility': 260.6, 'market_risk': 'High'},\n",
    "    'France': {'bev_volatility': 10.9, 'growth_volatility': 189.1, 'market_risk': 'Medium'},\n",
    "    'USA': {'bev_volatility': 6.4, 'growth_volatility': 114.5, 'market_risk': 'Low'},\n",
    "    'United Kingdom': {'bev_volatility': 11.7, 'growth_volatility': 137.2, 'market_risk': 'Medium-High'}\n",
    "}\n",
    "\n",
    "print(\"Market Risk Assessment (Policy Sensitivity):\")\n",
    "print(\"=\" * 60)\n",
    "print(\"Market          | BEV Volatility | Growth Volatility | Risk Level\")\n",
    "print(\"                | (Max pp change)| (Max YoY %)      |\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "for market, data in markets_analysis.items():\n",
    "    print(f\"{market:<15} | {data['bev_volatility']:>13.1f} | {data['growth_volatility']:>16.1f} | {data['market_risk']}\")\n",
    "\n",
    "print(\"\\n=== INVESTMENT STRATEGY MATRIX ===\")\n",
    "print(\"\\n🎯 STABLE GROWTH TARGETS (Low Policy Risk):\")\n",
    "print(\"   • USA: Consistent BEV preference + predictable growth\")\n",
    "print(\"   • Investment: BEV infrastructure, charging networks\")\n",
    "\n",
    "print(\"\\n📈 HIGH-REWARD/HIGH-RISK (Policy-Sensitive Growth):\")\n",
    "print(\"   • Germany: Massive volatility = opportunity + risk\")\n",
    "print(\"   • Investment: Flexible tech portfolio, policy hedging\")\n",
    "\n",
    "print(\"\\n⚖️ BALANCED PORTFOLIO MARKETS (Moderate Risk):\")\n",
    "print(\"   • France: Steady growth, manageable volatility\")\n",
    "print(\"   • UK: Strong growth but Brexit/policy uncertainty\")\n",
    "\n",
    "print(\"\\n=== MARKET ENTRY STRATEGIC RECOMMENDATIONS ===\")\n",
    "\n",
    "recommendations = {\n",
    "    \"Technology Strategy\": [\n",
    "        \"Portfolio approach: BEV primary + PHEV backup in policy-sensitive markets\",\n",
    "        \"USA: Can focus on BEV-only strategy due to policy stability\",\n",
    "        \"Europe: Maintain technology flexibility due to policy volatility\"\n",
    "    ],\n",
    "    \"Market Timing\": [\n",
    "        \"Germany: Enter during policy-favorable periods, expect volatility\",\n",
    "        \"France: Steady entry strategy, consistent growth trajectory\", \n",
    "        \"USA: Immediate market entry viable, stable policy environment\",\n",
    "        \"UK: Monitor post-Brexit policy stabilization\"\n",
    "    ],\n",
    "    \"Risk Management\": [\n",
    "        \"Diversify across markets with different policy cycles\",\n",
    "        \"Monitor policy indicators: incentive levels, taxation changes\",\n",
    "        \"Build scenario planning for policy-driven market shifts\"\n",
    "    ]\n",
    "}\n",
    "\n",
    "for category, items in recommendations.items():\n",
    "    print(f\"\\n{category}:\")\n",
    "    for item in items:\n",
    "        print(f\"   • {item}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3879a8e8-1520-48d3-a1bc-58ac97ab5ee6",
   "metadata": {},
   "source": [
    "## Summary\n",
    "## Executive Summary: Portfolio Case Study\n",
    "### Key Business Insights\n",
    "1. Market Opportunity Identification:\n",
    "- France: #1 investment opportunity (310K vehicles, 68% CAGR, stable growth)\n",
    "- Germany: High-reward/high-risk (180K vehicles, 88% CAGR, extreme volatility)\n",
    "- UAE/Romania: Explosive growth markets (200%+ CAGR, early-stage positioning)\n",
    "2. Technology Strategy Intelligence:\n",
    "- Policy drives technology mix more than consumer preference\n",
    "- USA: Most predictable BEV market (79% share, low volatility)\n",
    "- Europe: Requires technology portfolio flexibility due to policy sensitivity\n",
    "3. Market Dynamics Discovery:\n",
    "- Growth ≠ BEV adoption: Markets can expand while BEV share declines\n",
    "- Policy timing critical: Enter during incentive-favorable periods\n",
    "- Market maturity: Norway shows post-incentive decline patterns\n",
    "\n",
    "## Strategic Value Delivered\n",
    "- For Investors: Market opportunity scoring with policy risk assessment\n",
    "- For Manufacturers: Technology strategy guidance by market sensitivity\n",
    "- For Policymakers: Incentive effectiveness measurement and optimization\n",
    "\n",
    "## Conclusion: Market Intelligence for Strategic Decision-Making\n",
    "This comprehensive analysis of global EV markets reveals three critical insights for strategic decision-making:\n",
    "\n",
    "**Market Opportunity Hierarchy**: Clear differentiation between stable growth markets (USA), high-potential volatile markets (Germany/France), and explosive emerging markets (UAE/Romania) enables targeted investment strategies.\n",
    "\n",
    "**Policy-Driven Dynamics**: Technology adoption patterns reflect policy effectiveness more than consumer preference, requiring flexible portfolio approaches and policy-sensitive market timing.\n",
    "\n",
    "**Strategic Implementation**: Success requires matching market entry strategy to local policy cycles, maintaining technology portfolio flexibility, and building scenario planning capabilities for policy-driven market shifts.\n",
    "\n",
    "**Next Steps**: Stakeholders should prioritize market selection based on risk tolerance, develop policy monitoring capabilities, and build flexible technology portfolios aligned with regional adoption patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f246c994-149b-4084-aef7-a273979202fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "DATA SOURCE & METHODOLOGY\n",
      "============================================================\n",
      "Dataset: Electric Vehicle Sales (2010-2024)\n",
      "Coverage: 52 global regions, 7 market metrics, 4 powertrain types\n",
      "Analysis Period: 2010-2023 (14 years)\n",
      "Methodology: Time series analysis, growth rate calculation, strategic matrix positioning\n",
      "Quality: Zero missing values, 3,798 observations\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "# Data source citation\n",
    "print(\"=\" * 60)\n",
    "print(\"DATA SOURCE & METHODOLOGY\")\n",
    "print(\"=\" * 60)\n",
    "print(\"Dataset: Electric Vehicle Sales (2010-2024)\")\n",
    "print(\"Coverage: 52 global regions, 7 market metrics, 4 powertrain types\")\n",
    "print(\"Analysis Period: 2010-2023 (14 years)\")\n",
    "print(\"Methodology: Time series analysis, growth rate calculation, strategic matrix positioning\")\n",
    "print(\"Quality: Zero missing values, 3,798 observations\")\n",
    "print(\"=\" * 60)"
   ]
  }
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